Synthetic aperture radar (SAR) is able to provide high-resolution images of large areas under a wide range of operating conditions. The ability to recognise targets automatically in SAR images is therefore highly desirable, for both military and civil applications.
Delineation is the process of identifying the boundary between a target and the surrounding background or “clutter”. This is a critical stage of the processing chain because accurate knowledge of a target's boundary allows features to be recognised which strongly discriminate between different classes of man-made targets. It is known to segment SAR imagery into target, shadow, and background clutter regions in the process of recognizing targets. However, the effects of finite resolution, speckle and self-shadowing tend to make targets' boundaries extremely difficult to detect directly.
It also is known to utilise a region-based active contour model, in which a contour that partitions the image into disjoint regions evolves to maximise the agreement between these regions and a statistical model of the image.
Measures of a contour's fitness tend to have a large number of local maxima over the space of all possible contours. This is due both to the level of noise in typical SAR images and to the complexity of targets' apparent shapes. Most of these local maxima correspond to very poor delineations. In applying active contours to SAR images the robustness of the optimisation algorithm to spurious maxima is therefore important.
Simulated annealing is a stochastic optimisation algorithm, which can be robust to the presence of local maxima. It has been proposed to use simulated annealing to optimise the placement of active contours for delineating targets.
However we have found that the use of simulated annealing in a conventional way may not delineate the target quickly enough for real-time SAR applications. The present invention, at least in its preferred embodiment, seeks to provide more rapid target delineation.